Design data models and optimize performance in Azure DocumentDB
At a glance
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Build applications with Azure DocumentDB, a fully managed MongoDB-compatible database service. Create and configure clusters, then perform CRUD operations with the MongoDB Query Language. Connect your applications using Python, .NET, and JavaScript SDK. Design efficient data models and optimize query performance with indexing strategies.
Prerequisites
- Familiarity with general application development concepts.
- Understanding of JSON document structure.
- Basic database fundamentals such as collections, documents, and queries.
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Modules in this learning path
Explore the core features, architecture, and use cases for Azure DocumentDB, a fully managed MongoDB-compatible database service.
Learn how to provision an Azure DocumentDB cluster, connect with MongoDB tools, and configure compute, storage, and security settings.
Write queries to create, read, update, and delete documents in Azure DocumentDB. Build aggregation pipelines to transform and analyze data.
Build applications that connect to Azure DocumentDB using the official MongoDB drivers for Python, .NET, and JavaScript. Perform CRUD operations programmatically and integrate database operations into your application code.
Model data relationships effectively in Azure DocumentDB. Analyze entity relationships using access patterns and cardinality, apply the embed-vs-reference decision framework, and implement one-to-one, one-to-many, and many-to-many relationship patterns with practical e-commerce examples.
Apply 10 schema design patterns to solve data modeling challenges in Azure DocumentDB. Organize documents, precompute results, manage arrays, group time-series data, evolve schemas, and archive old data.
Identify and fix common schema design mistakes in Azure DocumentDB, including unbounded arrays, collection sprawl, unnecessary indexes, over-normalization, and case-sensitivity issues.
Optimize query performance in Azure DocumentDB by building effective indexing strategies. Create single-field, compound, multi-key, and specialized indexes. To maintain performance at scale, analyze query execution with the explain() command and monitor index health.